Defining what makes an object an object is hard.
In philosophy, Peirce defines an object as anything we can think and talk about [1]}.
In computer vision, object definitions for semantic segmentation are more pragmatic and feature various notions of objectness as well as different levels of granularity.
For instance, the COCO-Stuff benchmark distinguishes between stuff (objects without a clear shape) and things (objects with a “well-defined” shape) [2]}, [3]} and features coarse and fine object categories.
Others, like Cityscapes [4]}, choose a segmentation that is most informative for a specific application like autonomous driving and therefore also include sky and road as object classes.
